SAR image despeckling using a dilated densely connected network

Various type of deep convolution neural network (CNN) have been successfully applied to synthetic aperture radar (SAR) image despeckling. However, the model is limited by the length of network structure and expensive computation. In this paper, we proposed a novel deep learning-based approach learning the mapping from noisy image to noisy-free image with a dilated densely connected network (SAR-DDCN). Compared with the recently proposed despeckling methods, our method can achieve superior performance with less computational cost. Specifically, we replace standard convolution with dilated convolution, which could enlarge the receptive field and keep the size of feature maps at the same time. In addition, densely connected network are added to the network to alleviate the problem of gradient vanishing. Extensive experiments on synthetic and real SAR image show that the proposed method achieve better performance with respect to state-of-the-art-methods on both quantitative measurements and visual results.